Alterations in host innate immune function within the lung may predispose to opportunistic infections that cause great morbidity and mortality during HIV infection. Mathematical models are needed to help understand the complex interactions of cytokines, cytokine antagonists, and chemokines that affect the innate host immune response within the lung during HIV infection. To date, analyses have been limited to descriptions of how HIV affects single or small sets of these molecules, instead of the larger network that acts in a coordinated manner to affect host immunity. Our group has developed "reverse engineering" methods to model networks of interacting proteins and mRNA. We will use these reverse engineering methods to determine the structure of the cytokine/chemokine networks in the lung, how these are altered during HIV infection, and after treatment with highly active antiretroviral therapy (HAART). The reverse engineering algorithms suggest the most efficient way to resolve the network structure through an iterative process in which analysis of data from one experiment informs the design of the next experiment. Specifically, we will create mathematical models of cytokine/chemokine networks, using reverse engineering methods, in cell lines (U1 and U937) and in AMs from HIV+ subjects (with or without HAART) and HIV- subjects. This will be accomplished by performing a series of experiments in which we perturb different components of the cytokine/chemokine networks, measure changes in the components of the network, and apply the reverse engineering algorithms to determine the next most informative perturbations to resolve the network structure. The components of the network that will be both measured and perturbed include selected cytokines and chemokines, signaling pathway molecules, transcription factors, and mRNA for these moieties. Identification of the most central molecules in these linked systems may suggest new therapeutic or vaccine approaches to bolster the host immune response to HIV.